import sim
import klib
import plotly.express as px

策略结果展示

载入klib module 和 sim 模块可以快速进行策略结果分析。

pwd

'/home/ubuntu/work/klib'

# 输入Backtest或实盘log路径
backtest_log_path = "sample_log/run_backtest/build_pos-20220114-20220517-20295" 
s = sim.Sim(backtest_log_path)
df_summary = s.read_summary()

sample_log/run_backtest/build_pos-20220114-20220517-20295/backtest/account_settlement-20220114-20220517.csv
log file found.

查看交易记录

s.read_trades("20220106", "20220517") # 给定start_data, end_date

	exchange	order_ref	trade_id	instrument_id	strategy_id	account_id	direction	offset	price	qty
timestamp
2022-01-14 21:01:17.012	DCE	202201171234	123450101	i2209	1234	12345678	SELL	OPEN	688.0	2
2022-01-17 14:55:00.563	DCE	202201171235	123460101	i2209	1234	12345678	BUY	CLOSE_TODAY	677.0	2
2022-01-17 21:01:00.913	DCE	202201181234	124470101	i2209	1234	12345678	BUY	OPEN	680.0	4
2022-01-18 14:55:01.475	DCE	202201181235	124480101	i2209	1234	12345678	SELL	CLOSE_TODAY	685.5	4
2022-01-18 21:01:00.521	DCE	202201191234	125490101	i2209	1234	12345678	BUY	OPEN	692.0	1
...	...	...	...	...	...	...	...	...	...	...
2022-05-13 14:55:39.860	DCE	202205131235	127590101	i2209	1234	12345678	SELL	CLOSE_TODAY	824.0	100
2022-05-13 21:01:00.456	DCE	202205161234	128600101	i2209	1234	12345678	BUY	OPEN	822.5	25
2022-05-16 14:55:00.440	DCE	202205161235	128610101	i2209	1234	12345678	SELL	CLOSE_TODAY	827.5	25
2022-05-16 21:01:00.680	DCE	202205171234	129620101	i2209	1234	12345678	SELL	OPEN	839.5	17
2022-05-17 14:55:00.051	DCE	202205171235	129630101	i2209	1234	12345678	BUY	CLOSE_TODAY	832.0	17

134 rows × 10 columns

每日净收益(除去手续费)

px.bar(df_summary, df_summary.index, y = "net_pnl", title = "daily netpnl")

策略净值

pure_value = df_summary.balance/df_summary.pre_balance[0]
px.line(pure_value, title = "净值")

策略统计数据 Performance Metrics

brief_summary = klib.get_brief_summary(df_summary)
display(brief_summary)

	交易日	开始日	结束日	起始资金	结束资金	净值	胜率	盈亏比	总盈利	手续费	夏普	回撤
0	88	2022-01-14	2022-05-17	2000000.0	2986897.415	1.493449	0.454545	2.182277	986897.415	21952.585	2.86832	-121070.145